
import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
							import onnxruntime as ort

class 							_UpperCamelCase					(       _A  ,							unittest.TestCase	):






			'''simple docstring'''

			pass




@nightly
@require_onnxruntime
@require_torch_gpu
class 							_UpperCamelCase					(       unittest.TestCase	):






			'''simple docstring'''

			@property
			def    lowerCAmelCase__						(							self			:							Any   ):
							return (
							    "CUDAExecutionProvider",
							    {
							        "gpu_mem_limit": "15000000000",  # 15GB
							        "arena_extend_strategy": "kSameAsRequested",
							    },
							)

			@property
			def    lowerCAmelCase__						(							self			:							Optional[int]   ):
							UpperCamelCase_:				Optional[int]						=    ort.SessionOptions()
							UpperCamelCase_:				Optional[int]						=    False
							return options

			def    lowerCAmelCase__						(							self			:							Any   ):
							UpperCamelCase_:				Tuple						=    load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/in_paint/overture-creations-5sI6fQgYIuo.png"""   )
							UpperCamelCase_:				Optional[int]						=    load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""   )
							UpperCamelCase_:				Union[str, Any]						=    OnnxStableDiffusionInpaintPipeline.from_pretrained(
							    """runwayml/stable-diffusion-inpainting"""    ,						revision="""onnx"""    ,						safety_checker=snake_case_    ,						feature_extractor=snake_case_    ,						provider=self.gpu_provider    ,						sess_options=self.gpu_options    ,						)
							pipe.set_progress_bar_config(disable=snake_case_   )

							UpperCamelCase_:				List[Any]						=    """A red cat sitting on a park bench"""

							UpperCamelCase_:				List[str]						=    np.random.RandomState(0   )
							UpperCamelCase_:				int						=    pipe(
							    prompt=snake_case_    ,						image=snake_case_    ,						mask_image=snake_case_    ,						guidance_scale=7.5    ,						num_inference_steps=10    ,						generator=snake_case_    ,						output_type="""np"""    ,						)
							UpperCamelCase_:				Union[str, Any]						=    output.images
							UpperCamelCase_:				int						=    images[0, 255:258, 255:258, -1]

							assert images.shape == (1, 512, 512, 3)
							UpperCamelCase_:				str						=    np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]   )

							assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-3




			def    lowerCAmelCase__						(							self			:							List[Any]   ):
							UpperCamelCase_:				Optional[Any]						=    load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/in_paint/overture-creations-5sI6fQgYIuo.png"""   )
							UpperCamelCase_:				List[str]						=    load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""   )
							UpperCamelCase_:				Union[str, Any]						=    LMSDiscreteScheduler.from_pretrained(
							    """runwayml/stable-diffusion-inpainting"""    ,						subfolder="""scheduler"""    ,						revision="""onnx"""   )
							UpperCamelCase_:				Union[str, Any]						=    OnnxStableDiffusionInpaintPipeline.from_pretrained(
							    """runwayml/stable-diffusion-inpainting"""    ,						revision="""onnx"""    ,						scheduler=snake_case_    ,						safety_checker=snake_case_    ,						feature_extractor=snake_case_    ,						provider=self.gpu_provider    ,						sess_options=self.gpu_options    ,						)
							pipe.set_progress_bar_config(disable=snake_case_   )

							UpperCamelCase_:				Union[str, Any]						=    """A red cat sitting on a park bench"""

							UpperCamelCase_:				Tuple						=    np.random.RandomState(0   )
							UpperCamelCase_:				Optional[int]						=    pipe(
							    prompt=snake_case_    ,						image=snake_case_    ,						mask_image=snake_case_    ,						guidance_scale=7.5    ,						num_inference_steps=20    ,						generator=snake_case_    ,						output_type="""np"""    ,						)
							UpperCamelCase_:				Dict						=    output.images
							UpperCamelCase_:				Union[str, Any]						=    images[0, 255:258, 255:258, -1]

							assert images.shape == (1, 512, 512, 3)
							UpperCamelCase_:				Union[str, Any]						=    np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]   )

							assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-3








from ..utils import is_flax_available, is_torch_available


if is_torch_available():
							from .autoencoder_kl import AutoencoderKL
							from .controlnet import ControlNetModel
							from .dual_transformer_ad import DualTransformeraDModel
							from .modeling_utils import ModelMixin
							from .prior_transformer import PriorTransformer
							from .ta_film_transformer import TaFilmDecoder
							from .transformer_ad import TransformeraDModel
							from .unet_ad import UNetaDModel
							from .unet_ad import UNetaDModel
							from .unet_ad_condition import UNetaDConditionModel
							from .unet_ad_condition import UNetaDConditionModel
							from .vq_model import VQModel

if is_flax_available():
							from .controlnet_flax import FlaxControlNetModel
							from .unet_ad_condition_flax import FlaxUNetaDConditionModel
							from .vae_flax import FlaxAutoencoderKL






